A hybrid tool life prediction scheme in cloud architecture

Haw Ching Yang, Yu Yung Li, Min Nan Wu, Fan Tien Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper presents a cloud service scheme to predict tool wear. When lacking sufficient historical tool wear data for building a data-driven model, predicting tool life is challenging while under various cutting conditions with different tools and machines. On the basis of a hybrid tool wear model with dynamic neural network, this paper proposes a tool life prediction scheme for predicting tool wear by given cutting conditions and relevant tool wear features which extracted from sensing segment data. Experimental results show that the proposed scheme can assist factory users to predict various tool lifetimes well in the cloud-service environment while with the first tool samples for modeling.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PublisherIEEE Computer Society
Pages1160-1165
Number of pages6
ISBN (Electronic)9781509024094
DOIs
Publication statusPublished - 2016 Nov 14
Event2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States
Duration: 2016 Aug 212016 Aug 24

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2016-November
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
CountryUnited States
CityFort Worth
Period16-08-2116-08-24

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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